Join Barton Poulson for an in-depth discussion in this video Clustering in Orange, part of Data Science Foundations: Data Mining.
- [Teacher] I'm here in a blank schema in Orange,…and what I need to do is drag in various objects…that give the commands in the processes that we need.…The first thing is to actually…read in the data, read in the files,…so I'm gonna expand this menu over here,…and we'll just click on File and bring that in.…And then I can either bring in…the other objects one at a time…or I can start dragging and we can do a little right-click.…But I'm gonna double-click on this one first…to tell it what data we're using.…Now I've told it before that I'm gonna be using ClusterData.…
That's located on my desktop,…and you can get at it this way.…There it is.…I'll just press Cancel since I have it already.…And so it knows what I'm looking for.…From there, I need to let it know…that I'm gonna be using some of the columns…and some of them will serve different purposes.…I'm gonna just click and drag from right here.…When I let go, it brings this up,…and I'm gonna go to this first selection here…and go to Select Columns.…So when I get that, I can double-click on it,…
Barton Poulson covers data sources and types, the languages and software used in data mining (including R and Python), and specific task-based lessons that help you practice the most common data-mining techniques: text mining, data clustering, association analysis, and more. This course is an absolute necessity for those interested in joining the data science workforce, and for those who need to obtain more experience in data mining.
- Prerequisites for data mining
- Data mining using R, Python, Orange, and RapidMiner
- Data reduction
- Data clustering
- Anomaly detection
- Association analysis
- Regression analysis
- Sequence mining
- Text mining
Skill Level Beginner
Transitioning from Data Warehousing to Big Datawith Alan Simon1h 50m Intermediate
Manage Your Organization's Big Data Programwith Alan Simon1h 11m Intermediate
2. Data Reduction
5. Anomaly Detection
6. Association Analysis
7. Regression Analysis
8. Sequential Patterns
9. Text Mining
Next steps1m 18s
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